面向决策学习的理想抽象
Ideal Abstractions for Decision-Focused Learning
摘要：
我们提出了一种方法，制定简化抽象的机器学习系统，通过识别和利用决策的效用结构。机器学习任务通常涉及高维输出空间（例如，对图像中的每个像素或图中的节点的预测），即使较粗糙的输出通常足以用于下游决策（例如，图像的区域而不是像素）。开发人员经常手工设计输出空间的抽象，但许多抽象是可能的，并且不清楚模型的输出空间的选择如何影响其在下游决策中的有用性。我们提出了一种自动配置输出空间的方法，以尽量减少决策相关信息的损失。从几何的角度来看，我们制定了一个步骤的算法作为一个投影的概率单纯形，称为倍，最大限度地减少总损失的决策相关的信息在H-熵的意义。至关重要的是，在抽象的结果空间中学习需要更少的数据，从而导致决策质量的净改善。我们在两个领域展示了该方法：用于深度神经网络训练的数据采集和闭环野火管理任务。
英文摘要：
We present a methodology for formulating simplifying abstractions in machine learning systems by identifying and harnessing the utility structure of decisions. Machine learning tasks commonly involve high-dimensional output spaces (e.g., predictions for every pixel in an image or node in a graph), even though a coarser output would often suffice for downstream decision-making (e.g., regions of an image instead of pixels). Developers often hand-engineer abstractions of the output space, but numerous abstractions are possible and it is unclear how the choice of output space for a model impacts its usefulness in downstream decision-making. We propose a method that configures the output space automatically in order to minimize the loss of decision-relevant information. Taking a geometric perspective, we formulate a step of the algorithm as a projection of the probability simplex, termed fold, that minimizes the total loss of decision-related information in the H-entropy sense. Crucially, learning in the abstracted outcome space requires less data, leading to a net improvement in decision quality. We demonstrate the method in two domains: data acquisition for deep neural network training and a closed-loop wildfire management task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
